Blood flow and velocity through the cardiovascular system can be used to diagnose disorders, such as congenital heart disease and valve abnormalities. Accurate diagnosis of these disorders requires obtaining both qualitative assessment and accurate quantitative measurement of the blood velocity. Magnetic resonance (MR) is one tool used to measure the blood velocity.
Phase-contrast Cardiovascular Magnetic Resonance Imaging (CMR) is typically used to provide such accurate blood velocity measurements. Typically, velocity-encoded gradient waveforms are used to provide encoding of the velocity in the phase of the MR image. When compared against an un-encoded MR image, the difference in phase is proportional to the velocity. In addition, it is also often necessary to calibrate this velocity to a zero velocity baseline reference—e.g., that from stationary tissue adjacent to a blood vessel. However, such calibration is difficult to perform in CMR of the heart and great vessels because often there is no, or minimal, stationary tissue adjacent to the heart and large blood vessels. Therefore, uncorrected velocity offsets may introduce substantial errors in velocity flow quantification.
Background phase bias due to presence of eddy-currents and random noise can also adversely affect the quality of MR phase velocity measurements. Conventionally, background phase bias can be reduced by identifying static tissue within the phase contrast image. This criterion for static tissue is determined by calculating time standard deviations of the phase velocity images over a cardiac cycle, and identifying as static regions those portions with a low standard deviation. Because the vessels, or objects, of interest are far from static, using a conventional, linear-fitted velocity correction can result in under-fitting. Conversely, fitting with higher spatial orders can result in over-fitting because of the lack of data points near the vessels or objects of interest. The choice of basis functions for these higher spatial orders is not generally based on physical properties of the MRI scanner.